免疫克隆选择算法研究及其应用
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摘要
人工免疫系统是模拟生物免疫系统的高性能、自组织、强鲁棒性的人工智能系统。本文主要在深入探索和研究了生物免疫系统中蕴含的智能学习机制。提出了一种高效的免疫优势克隆选择算法模型；设计了一种面向组合优化问题的局部最优免疫优势克隆选择算法；并将免疫优势克隆选择算法与蚁群算法相结合构造了一种新的免疫蚁群克隆选择算法；最后将免疫智能算法与现代自抗扰控制理论相结合提出了一种免疫智能自抗扰控制技术理论与方法。主要研究工作包括以下几个方面：
(1)提出了一种免疫优势的克隆选择算法框架模型,将克隆倍增算子予以改进,改进了亲和力函数,算法通过局部最优免疫优势,克隆扩增,动态高频变异等相关算子的操作,同时引入浓度调节机制抗体克隆删除等操作保证抗体群的多样性,使算法在深度搜索和广度寻优之间取得了平衡,理论分析了算法的收敛性,实验比较验证算法的高效性。
(2)提出了一种面向组合优化问题的局部最优免疫优势克隆选择算法,设计出一种针对TSP问题的局部最优免疫优势算子,通过局部最优免疫优势算子操作获得优秀抗体,将优秀抗体按照亲和力和浓度大小进行克隆扩增,通过高频动态变异,提高抗体亲和力成熟的效率,同时引入浓度调节,与抗体克隆删除等相关算子避免了算法未成熟收敛。通过实验结果表明了该算法的可行性、有效性,具有较快的收敛速度,同时具有精确的最优解。
(3)将免疫优势克隆选择算法与蚁群算法相结合,提出了一种免疫蚁群克隆选择算法。该算法将蚂蚁分成两种状态,扩大了解的搜索空间,有效抑制了收敛过程中的早熟停滞现象,将禁忌表中的抗体通过克隆扩增、高频变异等免疫算子操作得到精英蚂蚁,再对抗体记忆库引入局部最优免疫策略。理论分析了算法的收敛性,并将其应用于组合优化问题求解,实验结果表明提高算法的求解精度。
(4)将免疫智能算法与现代自抗扰控制理论相结合,利用改进的免疫克隆选择算法的强优化能力,将其应用于自抗扰控制器优化,提出智能自抗扰控制器的优化方法和步骤,充分发挥了自抗扰控制器优越性,最后将免疫智能自抗扰控制器应用于非线性系统控制并与最近研究方法比较表明了免疫智能自抗扰控制器具有优良的控制品质,为智能控制理论提供了新的思路。
Artificial Immune Systems (AIS) is an artificial intelligent system, which simulating the high-performance, self-organization and robustness of biological immune system. The objective of this study is to explore the evolutionary learning mechanisms contained in biological immune system。A novel algorithm based on Immunodominance Clonal Selection Algorithm is proposed, the Local Optimization Immunodominance Clonal Selection Algorithm for combinatorial optimization problem is devised. Immune ant Clonal Selection Algorithm through combining Immunodominance Clonal Selection Algorithm and ant colony algorithm is proposed. At last, a theory and method of Intelligent Disturbance Rejection Control by integrating the immune Algorithm with Auto-Disturbance Rejection Controller Technique is presented. The study focuses on the following aspects:
(1) This paper proposes the model of Local Optimization immunodominance Clonal Selection Algorithm. Clonal multiplication operator and affinity function etal operator are improved. The affinity maturation of antibody is enhanced by local Optimization Immunodominance operating, clone expansion and dynamic hyper mutation and so on. Simultaneously, adjusting mechanism of antibody concentration and antibody clonal deletion are introduced into this algorithm, which enhances the diversity of antibody and get the balance between the depth and breadth research. Providing theoretical proof for the quicker convergence speed of Local Optimization immunodominance Clonal Selection Algorithm. Simulation testing illustrates that the algorithm has a remarkable quality of convergence velocity and global convergence reliability.
(2) This paper proposes a Local Optimization immunodominance Clonal Selection Algorithm for combinatorial optimization problem. Local Optimization immunodominanc operators are designed for Travelling Salesman Problem. Excellent antibodies are obtained by Local Optimization mmunodominance operating. The Multiple of clone according to the affinity and Concentration of antibodies. The affinity maturation of antibody is enhanced clone expansion and adaptive dynamic hyper mutation and so on. Adjusting mechanism of antibody concentration and antibody clonal deletion are introduced into this algorithm. Simulation testing illustrates that the algorithm is Feasible, efficient, has a remarkable quality of convergence velocity and global convergence reliability.
(3) The paper proposes immune ant Clonal Selection Algorithm through combining Immunodominance Clonal Selection Algorithm and ant colony algorithm. In order to enhance explorative capacity of the algorithm while avoiding the premature stagnation behavior, ants were divided into two groups with different state, elitist ants were got from tabu table which was optimized through immune operator like clone expansion and hyper mutation, etal, and then local optimization immunodominance operating was introduced into this algorithm. Providing theoretical proof for the quicker convergence speed of the algorithm, and then the algorithm is applied into combinatorial optimization problem, the experiments on TSP problems show that the new algorithm is capable of improving the search performance significantly no matter in convergent speed or precision.
(4) The Theory and Methods of Intelligent Disturbance Rejection Control by integrating the immune Algorithm with Auto-Disturbance Rejection Controller Technique are proposed. Utilizing the optimization-ability of the improved immune clonal selection algorithm. And then is applied to to optimize the Auto-Disturbance Rejection Controller.The optimization Methods and procedures of the Disturbance Rejection Control are proposed. Bring The Superiority of the Auto-Disturbance Rejection Controller Technique into full play. Simulation results of nonlinear discrete-time systems demonstrate that has excellent control quality. Providing new ideas for modern intelligent control.
(1)提出了一种免疫优势的克隆选择算法框架模型,将克隆倍增算子予以改进,改进了亲和力函数,算法通过局部最优免疫优势,克隆扩增,动态高频变异等相关算子的操作,同时引入浓度调节机制抗体克隆删除等操作保证抗体群的多样性,使算法在深度搜索和广度寻优之间取得了平衡,理论分析了算法的收敛性,实验比较验证算法的高效性。
(2)提出了一种面向组合优化问题的局部最优免疫优势克隆选择算法,设计出一种针对TSP问题的局部最优免疫优势算子,通过局部最优免疫优势算子操作获得优秀抗体,将优秀抗体按照亲和力和浓度大小进行克隆扩增,通过高频动态变异,提高抗体亲和力成熟的效率,同时引入浓度调节,与抗体克隆删除等相关算子避免了算法未成熟收敛。通过实验结果表明了该算法的可行性、有效性,具有较快的收敛速度,同时具有精确的最优解。
(3)将免疫优势克隆选择算法与蚁群算法相结合,提出了一种免疫蚁群克隆选择算法。该算法将蚂蚁分成两种状态,扩大了解的搜索空间,有效抑制了收敛过程中的早熟停滞现象,将禁忌表中的抗体通过克隆扩增、高频变异等免疫算子操作得到精英蚂蚁,再对抗体记忆库引入局部最优免疫策略。理论分析了算法的收敛性,并将其应用于组合优化问题求解,实验结果表明提高算法的求解精度。
(4)将免疫智能算法与现代自抗扰控制理论相结合,利用改进的免疫克隆选择算法的强优化能力,将其应用于自抗扰控制器优化,提出智能自抗扰控制器的优化方法和步骤,充分发挥了自抗扰控制器优越性,最后将免疫智能自抗扰控制器应用于非线性系统控制并与最近研究方法比较表明了免疫智能自抗扰控制器具有优良的控制品质,为智能控制理论提供了新的思路。
Artificial Immune Systems (AIS) is an artificial intelligent system, which simulating the high-performance, self-organization and robustness of biological immune system. The objective of this study is to explore the evolutionary learning mechanisms contained in biological immune system。A novel algorithm based on Immunodominance Clonal Selection Algorithm is proposed, the Local Optimization Immunodominance Clonal Selection Algorithm for combinatorial optimization problem is devised. Immune ant Clonal Selection Algorithm through combining Immunodominance Clonal Selection Algorithm and ant colony algorithm is proposed. At last, a theory and method of Intelligent Disturbance Rejection Control by integrating the immune Algorithm with Auto-Disturbance Rejection Controller Technique is presented. The study focuses on the following aspects:
(1) This paper proposes the model of Local Optimization immunodominance Clonal Selection Algorithm. Clonal multiplication operator and affinity function etal operator are improved. The affinity maturation of antibody is enhanced by local Optimization Immunodominance operating, clone expansion and dynamic hyper mutation and so on. Simultaneously, adjusting mechanism of antibody concentration and antibody clonal deletion are introduced into this algorithm, which enhances the diversity of antibody and get the balance between the depth and breadth research. Providing theoretical proof for the quicker convergence speed of Local Optimization immunodominance Clonal Selection Algorithm. Simulation testing illustrates that the algorithm has a remarkable quality of convergence velocity and global convergence reliability.
(2) This paper proposes a Local Optimization immunodominance Clonal Selection Algorithm for combinatorial optimization problem. Local Optimization immunodominanc operators are designed for Travelling Salesman Problem. Excellent antibodies are obtained by Local Optimization mmunodominance operating. The Multiple of clone according to the affinity and Concentration of antibodies. The affinity maturation of antibody is enhanced clone expansion and adaptive dynamic hyper mutation and so on. Adjusting mechanism of antibody concentration and antibody clonal deletion are introduced into this algorithm. Simulation testing illustrates that the algorithm is Feasible, efficient, has a remarkable quality of convergence velocity and global convergence reliability.
(3) The paper proposes immune ant Clonal Selection Algorithm through combining Immunodominance Clonal Selection Algorithm and ant colony algorithm. In order to enhance explorative capacity of the algorithm while avoiding the premature stagnation behavior, ants were divided into two groups with different state, elitist ants were got from tabu table which was optimized through immune operator like clone expansion and hyper mutation, etal, and then local optimization immunodominance operating was introduced into this algorithm. Providing theoretical proof for the quicker convergence speed of the algorithm, and then the algorithm is applied into combinatorial optimization problem, the experiments on TSP problems show that the new algorithm is capable of improving the search performance significantly no matter in convergent speed or precision.
(4) The Theory and Methods of Intelligent Disturbance Rejection Control by integrating the immune Algorithm with Auto-Disturbance Rejection Controller Technique are proposed. Utilizing the optimization-ability of the improved immune clonal selection algorithm. And then is applied to to optimize the Auto-Disturbance Rejection Controller.The optimization Methods and procedures of the Disturbance Rejection Control are proposed. Bring The Superiority of the Auto-Disturbance Rejection Controller Technique into full play. Simulation results of nonlinear discrete-time systems demonstrate that has excellent control quality. Providing new ideas for modern intelligent control.
引文
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